# Network Plasticity as Bayesian Inference

arXiv: https://arxiv.org/abs/1504.05143 · HTML: https://mlsanity.com/paper/1504.05143/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2015.

## Built on by (most cited first, 13 of 13)

- [Pruning of Deep Spiking Neural Networks through Gradient Rewiring](https://mlsanity.com/paper/2105.04916/) (2021)
- [ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks](https://mlsanity.com/paper/2306.03693/) (2023)
- [Bayesian Continual Learning via Spiking Neural Networks](https://mlsanity.com/paper/2208.13723/) (2022)
- [Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning](https://mlsanity.com/paper/2406.01072/) (2024)
- [Desynchronous Learning in a Physics-Driven Learning Network](https://mlsanity.com/paper/2201.04626/) (2022)
- [Probabilistic Modeling: Proving the Lottery Ticket Hypothesis in Spiking Neural Network](https://mlsanity.com/paper/2305.12148/) (2023)
- [Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks](https://mlsanity.com/paper/2311.16141/) (2023)
- [Log-Normal Multiplicative Dynamics for Stable Low-Precision Training of Large Networks](https://mlsanity.com/paper/2506.17768/) (2025)
- [Contribution of task-irrelevant stimuli to drift of neural representations](https://mlsanity.com/paper/2510.21588/) (2025)
- [Neuromorphic Intelligence](https://mlsanity.com/paper/2509.11940/) (2025)
- [Locally Learned Synaptic Dropout for Complete Bayesian Inference](https://mlsanity.com/paper/2111.09780/) (2021)
- [Membrane Potential Distribution Adjustment and Parametric Surrogate Gradient in Spiking Neural Networks](https://mlsanity.com/paper/2304.13289/) (2023)
- [A Unified Perspective on Optimization in Machine Learning and Neuroscience: From Gradient Descent to Neural Adaptation](https://mlsanity.com/paper/2510.18812/) (2025)

## Abstract (arXiv)

General results from statistical learning theory suggest to understand not only brain computations, but also brain plasticity as probabilistic inference. But a model for that has been missing. We propose that inherently stochastic features of synaptic plasticity and spine motility enable cortical networks of neurons to carry out probabilistic inference by sampling from a posterior distribution of network configurations. This model provides a viable alternative to existing models that propose convergence of parameters to maximum likelihood values. It explains how priors on weight distributions and connection probabilities can be merged optimally with learned experience, how cortical networks can generalize learned information so well to novel experiences, and how they can compensate continuously for unforeseen disturbances of the network. The resulting new theory of network plasticity explains from a functional perspective a number of experimental data on stochastic aspects of synaptic plasticity that previously appeared to be quite puzzling.